A Personalized Social Force-based Lane-changing Model with a New Optimizer

Understanding and replicating driver lane-changing behavior is essential for developing advanced driver assistance systems that align with driver expectations and for constructing dynamic, realistic traffic flow scenarios in autonomous driving tests. This study proposes a two-dimensional, force-based lane-changing dynamics model that represents vehicle motion as acceleration changes under the influence of (surrounding) social forces. The model captures longitudinal and lateral interactions with neighboring vehicles and incorporates personalized parameters to reflect trajectory-level heterogeneity. A temporal attention mechanism is introduced to model the dynamic shift of driver focus during lane change, ensuring continuity in interaction forces. For parameter calibration, AdaP+ is developed, a gradient-based optimizer that integrates Nesterov momentum, decoupled weight decay, and gradient belief within the Adam framework, and improved with a pruning strategy for better initialization and faster convergence. Experimental findings on the HighD dataset demonstrate that the social force lane change model outperforms baselines in both trajectory fitting and behavioral simulation. Gradient-based optimizers, particularly AdaP+, achieve higher precision than swarm intelligence algorithms. Overall, the proposed model accurately replicates complex lane-change dynamics, while the integration of personalized parameters and attention mechanism effectively captures trajectory-level heterogeneity and interaction continuity, with potential to support naturalistic, personalized lane-change trajectory generation in future connected-vehicle settings.